this work was partially supported by the joint dms/nigms initiative to support research in the area...

This work was partially supported by the Joint DMS/NIGMS Initiative to Support Research in the Area of Mathematical Biology (NSF 0800285).

Isabel K. DarcyMathematics Department Applied Mathematical and Computational Sciences (AMCS)University of Iowahttp://www.math.uiowa.edu/~idarcy

http://www.ima.umn.edu/2008-2009/ND6.15-26.09/

Create your own homology

3 ingredients:

1.) Objects

2.) Grading

3.) Boundary map

v2

e2e1

e3

v1 v3

2-simplex = triangle

1-simplex = edge

ev1 v2

0-simplex = vertex = v

Building blocks for a simplicial homology

GradingGrading: Each object is assigned a unique gradeGrading = Partition of R[x]

Ex: Grade = dimension

v2

e2e1

e3v1 v3

Grade 2: 2-simplex = triangle = {v1, v2, v3}

Grade 1: 1-simplex = edge = {v1, v2} ev1 v2

Grade 0: 0-simplex = vertex = v

Boundary Map

n : Cn Cn-1 such that 2 = 0

v2

e2e1

e3

v1 v3

ev1 v2

0

0

v1 v2

v2

e2e1

e3

v1 v3

Cn+1 Cn Cn-1 . . . C2 C1 C0 0

Hn = Zn/Bn = (kernel of )/ (image of )

cycles

boundaries=

n+1

n+1n

n 2 1 0

v2

e2e1

e3

v1 v3

Čech homologyGiven U Va where Va open for all a in A.

Objects = finite intersections = { V a : ai in A }

Grading = n = depth of intersection.

( V a ) = S Va

Ex: (Va) = 0, (V a Vb) = Va + Vb

(V a V b Vg) = (Va Vb) + (Va Vg) + (Vb Vg)

U

i = 1

n

i

a in A

n+1 j = 1

n

i i

U

i = 1 i ≠ j

nU

i = 1

n ( )0 1

U

U U U U U

2

Your name homology3 ingredients:

1.) Objects

2.) Grading

3.) Boundary mapn : Cn Cn-1 such that 2 = 0

Creating a simplicial complex from Data

Step 0.) Start by adding data points = 0-dimensional vertices (0-simplices)

Creating a simplicial complex from Data

Step 0.) Start by adding 0-dimensional vertices (0-simplices)

Creating a simplicial complex from Data

0.) Start by adding 0-dimensional data points Note: we only need a definition of closeness between data points. The data points do not need to be actual points in Rn

Creating a simplicial complex from Data

0.) Start by adding 0-dimensional data points Note: we only need a definition of closeness between data points. The data points do not need to be actual points in Rn

(1, 8)

(1, 5)(2, 7)

Creating a simplicial complex from Data

0.) Start by adding 0-dimensional data points Note: we only need a definition of closeness between data points. The data points do not need to be actual points in Rn

(dog, happy)

(dog, content)

(wolf, mirthful)

Creating a simplicial complex from Data

1.) Adding 1-dimensional edges (1-simplices)Add an edge between data points that are “close”

Creating a simplicial complex from Data

1.) Adding 1-dimensional edges (1-simplices)Add an edge between data points that are “close”

Creating a simplicial complex from Data

1.) Adding 1-dimensional edges (1-simplices)Let T = Threshold =Connect vertices v and w with an edge iff the distance between v and w is less than T

Creating a simplicial complex from Data

1.) Adding 1-dimensional edges (1-simplices)Add an edge between data points that are “close”

Creating a simplicial complex from Data

1.) Adding 1-dimensional edges (1-simplices)Add an edge between data points that are “close”

Creating the Vietoris Rips simplicial complex

2.) Add all possible simplices of dimensional > 1.

0.) Start by adding 0-dimensional data points Note: we only need a definition of closeness between data points. The data points do not need to be actual points in Rn

Creating the Vietoris Rips simplicial complex

H0 counts clusters

H0 counts clusters

0.) Start by adding 0-dimensional data points Note: we only need a definition of closeness between data points. The data points do not need to be actual points in Rn

Creating the Vietoris Rips simplicial complex

Cycl

es

Time

Instead of growing balls, we have a growing path (along with the cover of the path)

0.) Start by adding 0-dimensional data points Note: we only need a definition of closeness between data points. The data points do not need to be actual points in Rn

Creating the Vietoris Rips simplicial complex

Constructing functional brain networks with 97 regions of interest (ROIs) extracted from FDG-PET data for 24 attention-deficit hyperactivity disorder (ADHD),26 autism spectrum disorder (ASD) and11 pediatric control (PedCon).

Data = measurement fj taken at region j

Graph: 97 vertices representing 97 regions of interest edge exists between two vertices i,j if correlation between fj and fj ≥ threshold

How to choose the threshold? Don’t, instead use persistent homology

Discriminative persistent homology of brain networks, 2011 Hyekyoung Lee Chung, M.K.; Hyejin Kang; Bung-Nyun Kim;Dong Soo Lee

Vertices = Regions of Interest

Create Rips complex by growing epsilon balls (i.e. decreasing threshold) where distance between two vertices is given by

where fi = measurement at

location i

Constructing functional brain networks with 97 regions of interest (ROIs) extracted from FDG-PET data for 24 attention-deficit hyperactivity disorder (ADHD),26 autism spectrum disorder (ASD) and11 pediatric control (PedCon).

Data = measurement fj taken at region j

Graph: 97 vertices representing 97 regions of interest edge exists between two vertices i,j if correlation between fj and fj ≥ threshold

How to choose the threshold? Don’t, instead use persistent homology

Discriminative persistent homology of brain networks, 2011 Hyekyoung Lee Chung, M.K.; Hyejin Kang; Bung-Nyun Kim;Dong Soo Lee

http://www.ima.umn.edu/videos/?id=856http://ima.umn.edu/2008-2009/ND6.15-26.09/activities/Carlsson-Gunnar/imafive-handout4up.pdf

http://www.ima.umn.edu/videos/?id=1846http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Carlsson-Gunnar/imamachinefinal.pdf

Application to Natural Image StatisticsWith V. de Silva, T. Ishkanov, A. Zomorodian

An image taken by black and white digital camera can be viewed as a vector, with one coordinate for each pixel

Each pixel has a “gray scale” value, can be thought of as a real number (in reality, takes one of 255 values)

Typical camera uses tens of thousands of pixels, so images lie in a very high dimensional space, call it pixel space, P

Lee-Mumford-Pedersen [LMP] study only high contrast patches.

Collection: 4.5 x 106 high contrast patches from acollection of images obtained by van Hateren and van der Schaaf

Eurographics Symposium on Point-Based Graphics (2004)Topological estimation using witness complexesVin de Silva and Gunnar Carlsson

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